One of the central questions in the epistemology of conversational AIs is how to classify the beliefs acquired from them. Two promising candidates are instrument-based and testimony-based beliefs. However, the category of instrument-based beliefs faces an intrinsic problem, and a challenge arises in its application. On the other hand, relying solely on the category of testimony-based beliefs does not encompass the totality of our practice of using conversational AIs. To address these limitations, I propose a novel classification of beliefs that (...) shifts the focus from the properties of the conversational AIs themselves to how recipients perceive AI output, specifically whether they take the output at face value. The proposed categories are beliefs by regarding as instruments and beliefs by regarding as testifiers. By using these complementary categories, a more comprehensive understanding of beliefs acquired from conversational AIs can be achieved while avoiding the problems associated with the traditional classification. (shrink)

Social knowledge, for the most part, is knowledge through testimony. This essay is an overview of the epistemology of testimony. The essay separates knowledge from justification, characterizes testimony as a source of belief, explains why testimony is a source of knowledge, canvasses arguments for anti-reductionism and for reductionism in the reductionism vs. anti-reductionism debate, addresses counterexamples to knowledge transmission, defends a safe basis account of testimonial knowledge, and turns to social norms as a partial explanation for the reliability of testimony.

The ability to interact in a natural language profoundly changes devices’ interfaces and potential applications of speaking technologies. Concurrently, this phenomenon challenges our mainstream theories of knowledge, such as how to analyze linguistic outputs of devices under existing anthropocentric theoretical assumptions. In section 1, I present the topic of machines that speak, connecting between Descartes and Generative AI. In section 2, I argue that accepted testimonial theories of knowledge and justification commonly reject the possibility that a speaking technological artifact can (...) give testimony. In section 3, I identify three assumptions underlying the view that rejects conversational AIs – AI-based technologies that converse, as testifiers: conversational AIs (1) lack intentions, (2) cannot be normatively assessed, and (3) cannot constitute an object in trust relations, while humans can. In section 4, I propose the concept ‘AI-testimony’ for analyzing outputs of conversational AIs, suggesting three conditions for technologies to deliver AI-testimony: (1) content is propositional, (2) generated and delivered with no other human directly involved, (3) the output is perceived as phenomenologically similar to that of a human. I conclude that this concept overcomes the limitations of the anthropocentric concept of testimony, opening future directions of research without associating conversational AIs with human-like agency. (shrink)

Pessimists about trust in the normative domain believe that forming normative beliefs on the basis of trusting others is problematic, forming normative beliefs in other ways is not so problematic and forming non-normative beliefs on the basis of trust is not so problematic. Whilst there is substantial disagreement over the best way of accounting for pessimist ideas about trust, it is widely accepted that the intuitively problematic character of forming normative beliefs on the basis of trust cannot be explained in (...) terms of the idea that trust cannot be a source of normative knowledge. This paper argues that the dismissal of scepticism about trust as a source of normative knowledge is unwarranted. It does so by developing a sceptical explanation of pessimist ideas, arguing that the obvious arguments against the sceptical explanation fail and arguing that the sceptical explanation has the resources to resolve the considerations that present problems for existing explanations. (shrink)

Forming a belief on the basis of accepting another’s testimony often involves a kind of reliance on the (say-so of the) testifier. I argue that this reliance has epistemically relevant features that cannot be represented in most mainstream theories in the epistemology of testimony. The targeted views are those that embrace individualism about testimonial justification.

The Exchange of Words, by MoranRichard. Oxford: OUP, 2018. Pp. 254.

I define a social norm as a regularity in behavior whose persistence is causally explained by the existence of sanctioning attitudes of participants toward violations—without these sanctions, individuals have motive to violate the norm. I show how a universal precept "When in circumstances S, do action F" can be sustained by the conditional preference of each to conform, given that others do, of a convention, and also reinforced by the sanctions of a norm. I observe that a precept with moral (...) force can be reinforced by a social norm. I then consider constitutive norms and show by means of an example, competitive figure skating, how a type of activity or practice G can have a constitutive norm NG. An ongoing activity in a community is engagement in that practice only if NG is reinforced as a social norm by participants. I apply this to the case of assertion: the speech act type Assertion has a constitutive norm NA, and a practice of making speech acts in a community is one of making assertions only if it is controlled by NA enforced by the sanctions of a social norm. (shrink)

Testimony poses a challenge to systematic epistemology. I cite two kinds of testimony situation where the recipient's belief is not safe, yet intuitively counts as knowledge. Can Sosa's more sophisticated virtue reliabilism, which theorises animal knowledge as apt belief, yield the intuitively correct verdict on these cases? Sosa shows that a belief can be apt, though it is not safe, and so it may seem a quick positive answer is forthcoming. However, I explore complications in applying his AAA framework, regarding (...) what we take as the circumstances in which the subject's attempt is made: the AAA framework does not mandate a particular choice, yet this affects whether the attempt comes out as apt or not. I conclude that Sosa's theory is subject to a familiar charge: it does not give a reductive account of knowledge, since we must deploy independent intuitions about whether knowledge is gained in a case, in order to apply it. (shrink)

Speaking with conversational AIs, technologies whose interfaces enable human-like interaction based on natural language, has become a common phenomenon. During these interactions, people form their beliefs due to the say-so of conversational AIs. In this paper, I consider, and then reject, the concepts of testimony-based beliefs and instrument-based beliefs as suitable for analysis of beliefs acquired from these technologies. I argue that the concept of instrument-based beliefs acknowledges the non-human agency of the source of the belief. However, the analysis focuses (...) on perceiving signs and indicators rather than content expressed in natural language. At the same time, the concept of testimony-based beliefs does refer to natural language propositions, but there is an underlying assumption that the agency of the testifier is human. To fill the lacuna of analyzing belief acquisition from conversational AIs, I suggest a third concept: technology-based beliefs. It acknowledges the non-human agency-status of the originator of the belief. Concurrently, the focus of analysis is on the propositional content that forms the belief. Filling the lacuna enables analysis that considers epistemic, ethical, and social issues of conversational AIs without excluding propositional content or compromising accepted assumptions about the agency of technologies. (shrink)